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from typing import Iterable, Sequence
import torch
from torch import nn
from torch.nn import functional as F
def squash(x: torch.Tensor, dim: int = -1, eps: float = 1e-7) -> torch.Tensor:
"""Squash nonlinearity used by capsule networks."""
squared_norm = (x * x).sum(dim=dim, keepdim=True)
scale = squared_norm / (1.0 + squared_norm)
return scale * x / torch.sqrt(squared_norm + eps)
class ConvBNAct(nn.Module):
"""Convolution + BatchNorm + SiLU."""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
padding: int | None = None,
) -> None:
super().__init__()
if padding is None:
padding = kernel_size // 2
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
self.bn = nn.BatchNorm2d(out_channels)
self.act = nn.SiLU(inplace=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.act(self.bn(self.conv(x)))
class PrimaryCaps2d(nn.Module):
"""Primary capsule layer for 2D feature maps."""
def __init__(
self,
in_channels: int,
num_caps: int,
dim_caps: int,
kernel_size: int = 1,
stride: int = 1,
padding: int | None = None,
) -> None:
super().__init__()
if padding is None:
padding = kernel_size // 2
out_channels = num_caps * dim_caps
self.num_caps = num_caps
self.dim_caps = dim_caps
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
self.bn = nn.BatchNorm2d(out_channels)
self.act = nn.SiLU(inplace=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.act(self.bn(self.conv(x)))
bsz, _, h, w = x.shape
x = x.view(bsz, self.num_caps, self.dim_caps, h, w)
return squash(x, dim=2)
class RoutingCaps(nn.Module):
"""Dynamic routing between capsules."""
def __init__(
self,
num_in_caps: int,
dim_in: int,
num_out_caps: int,
dim_out: int,
routing_iters: int = 3,
) -> None:
super().__init__()
self.num_in_caps = num_in_caps
self.dim_in = dim_in
self.num_out_caps = num_out_caps
self.dim_out = dim_out
self.routing_iters = routing_iters
weight = torch.randn(1, num_in_caps, num_out_caps, dim_out, dim_in) * 0.01
self.W = nn.Parameter(weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if x.ndim != 3:
raise ValueError(f"RoutingCaps expects [B, N, D], got {tuple(x.shape)}")
bsz = x.shape[0]
x = x.unsqueeze(2).unsqueeze(-1) # [B, N, 1, D, 1]
u_hat = torch.matmul(self.W, x).squeeze(-1) # [B, N, M, Dout]
b = x.new_zeros(bsz, self.num_in_caps, self.num_out_caps)
for idx in range(self.routing_iters):
c = F.softmax(b, dim=-1)
s = (c.unsqueeze(-1) * u_hat).sum(dim=1)
v = squash(s, dim=-1)
if idx < self.routing_iters - 1:
b = b + (u_hat * v.unsqueeze(1)).sum(dim=-1)
return v
class DeformableCaps2d(nn.Module):
"""Deformable capsule layer with learned sampling offsets."""
def __init__(
self,
in_channels: int,
num_child_caps: int = 8,
dim_child: int = 8,
num_parent_caps: int = 8,
dim_parent: int = 8,
num_samples: int = 4,
routing_iters: int = 3,
offset_scale: float = 1.0,
out_channels: int | None = None,
) -> None:
super().__init__()
self.num_child_caps = num_child_caps
self.dim_child = dim_child
self.num_parent_caps = num_parent_caps
self.dim_parent = dim_parent
self.num_samples = num_samples
self.routing_iters = routing_iters
self.offset_scale = offset_scale
self.primary = PrimaryCaps2d(in_channels, num_child_caps, dim_child, kernel_size=1, stride=1, padding=0)
self.offset = nn.Conv2d(in_channels, 2 * num_samples, kernel_size=3, stride=1, padding=1)
nn.init.zeros_(self.offset.weight)
nn.init.zeros_(self.offset.bias)
self.routing = RoutingCaps(
num_in_caps=num_samples * num_child_caps,
dim_in=dim_child,
num_out_caps=num_parent_caps,
dim_out=dim_parent,
routing_iters=routing_iters,
)
caps_channels = num_parent_caps * dim_parent
self.out_channels = out_channels or caps_channels
self.project = None
if self.out_channels != caps_channels:
self.project = ConvBNAct(caps_channels, self.out_channels, kernel_size=1, stride=1, padding=0)
@staticmethod
def _base_grid(h: int, w: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
ys = torch.linspace(-1.0, 1.0, h, device=device, dtype=dtype)
xs = torch.linspace(-1.0, 1.0, w, device=device, dtype=dtype)
yy, xx = torch.meshgrid(ys, xs, indexing="ij")
return torch.stack((xx, yy), dim=-1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
child = self.primary(x) # [B, Nc, Dc, H, W]
bsz, _, _, h, w = child.shape
child_flat = child.view(bsz, self.num_child_caps * self.dim_child, h, w)
offsets = self.offset(x).view(bsz, self.num_samples, 2, h, w)
offsets = torch.tanh(offsets) * self.offset_scale
scale_x = max(w - 1, 1) / 2.0
scale_y = max(h - 1, 1) / 2.0
scale = offsets.new_tensor([scale_x, scale_y]).view(1, 1, 2, 1, 1)
offsets = offsets / scale
base = self._base_grid(h, w, x.device, x.dtype).view(1, 1, h, w, 2)
grids = base + offsets.permute(0, 1, 3, 4, 2)
sampled = []
for idx in range(self.num_samples):
grid = grids[:, idx]
feat = F.grid_sample(
child_flat,
grid,
mode="bilinear",
padding_mode="zeros",
align_corners=True,
)
sampled.append(feat)
sampled = torch.stack(sampled, dim=1)
sampled = sampled.view(bsz, self.num_samples, self.num_child_caps, self.dim_child, h, w)
sampled = sampled.permute(0, 4, 5, 1, 2, 3).contiguous()
sampled = sampled.view(bsz * h * w, self.num_samples * self.num_child_caps, self.dim_child)
routed = self.routing(sampled)
routed = routed.view(bsz, h, w, self.num_parent_caps, self.dim_parent)
routed = routed.permute(0, 3, 4, 1, 2).contiguous()
out = routed.view(bsz, self.num_parent_caps * self.dim_parent, h, w)
if self.project is not None:
out = self.project(out)
return out
class DeformableCapsBlock(nn.Module):
"""Backbone block: Conv downsample + deformable capsule routing."""
def __init__(
self,
c1: int,
c2: int,
num_child_caps: int = 8,
dim_child: int = 8,
num_parent_caps: int = 8,
dim_parent: int = 8,
num_samples: int = 4,
routing_iters: int = 3,
stride: int = 1,
) -> None:
super().__init__()
self.down = ConvBNAct(c1, c2, kernel_size=3, stride=stride)
self.caps = DeformableCaps2d(
in_channels=c2,
num_child_caps=num_child_caps,
dim_child=dim_child,
num_parent_caps=num_parent_caps,
dim_parent=dim_parent,
num_samples=num_samples,
routing_iters=routing_iters,
out_channels=c2,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.down(x)
return self.caps(x)
class CapsuleBackbone(nn.Module):
"""Simple capsule-based backbone that returns multi-scale features."""
def __init__(
self,
in_channels: int = 3,
stem_channels: int = 64,
stages: Sequence[int] = (128, 256, 512),
capsule_cfgs: Iterable[dict] | None = None,
) -> None:
super().__init__()
self.stem = ConvBNAct(in_channels, stem_channels, kernel_size=3, stride=2)
stage_cfgs = list(capsule_cfgs) if capsule_cfgs is not None else [{}] * len(stages)
if len(stage_cfgs) != len(stages):
raise ValueError("capsule_cfgs must match stages length")
blocks = []
in_ch = stem_channels
for out_ch, cfg in zip(stages, stage_cfgs):
blocks.append(
nn.Sequential(
ConvBNAct(in_ch, out_ch, kernel_size=3, stride=2),
DeformableCaps2d(out_ch, out_channels=out_ch, **cfg),
)
)
in_ch = out_ch
self.stages = nn.ModuleList(blocks)
def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, ...]:
x = self.stem(x)
outputs = []
for stage in self.stages:
x = stage(x)
outputs.append(x)
return tuple(outputs)
__all__ = [
"CapsuleBackbone",
"ConvBNAct",
"DeformableCaps2d",
"DeformableCapsBlock",
"PrimaryCaps2d",
"RoutingCaps",
"squash",
]
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